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Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets linking to news for generating extractive summary of each document. We reveal the very basic value of tweets that can be utilized by regarding every tweet as a vote for candidate sentences. Base on such finding, we resort to unsupervised summarization models by leveraging the linking tweets to master the ranking of candidate extracts via random walk on a heterogeneous graph. Thedoi:10.1145/2766462.2767835 dblp:conf/sigir/WeiG15 fatcat:kalwqwdsdrczxnj4agbnlm75yq